QA Analyst AI/ML

Key responsibilities

• Create test strategies and test plans adapted to AI/ML-based systems.
• Design, execute, and maintain manual and exploratory test cases covering functional, non-functional, and AI-specific scenarios.
• Perform backlog analysis to assess testing feasibility and contribute to the overall testing strategy definition.
• Participate in estimation of test effort and risk identification, considering AI model uncertainty and data variability.
• Support UAT resources and activities, ensuring business requirements are correctly reflected in AI outputs.
• Review and analyze test results, including model outputs, prediction accuracy, and regression impacts.
• Identify, track, and report bugs/defects; validate fixes and monitor model performance over time.
• Prepare test status reports and issue status reports.
• Validate AI model behavior for correctness, robustness, fairness, and explainability.
• Design and execute data quality checks and data pipeline validation scenarios.
• Challenge AI model requirements and acceptance criteria with product owners.
• Contribute to the definition of AI quality metrics and KPIs.
• Actively participate in Agile SCRUM ceremonies.
• Keep up to date with AI/ML industry trends and QA best practices.
• Support more junior analysts by sharing knowledge and providing constructive feedback.

The candidate must have at least 3 years of experience as a QA Analyst.

• Hands-on experience in AI/ML, including supervised and unsupervised learning, model training/inference, and datasets.
• Good communication skills and the ability to synthesize and document clearly.
• Detail oriented, proactive, and autonomous.
• Experience with JIRA/XRAY.
• Good level of English.
• Higher education in Computer Science, Software Engineering, Data Science, or equivalent.
• Experience in an Agile SCRUM context.